Superpixel algorithms represent a very useful and increasingly popular preprocessing step for a wide range of computer vision applications (segmentation, image parsing, classification etc.). Grouping similar pixels into so called superpixels leads to a major reduction of the image primitives, i.e. of the features that allow a complete description of an image, which results in an increased computational efficiency for subsequent processing steps or allows for more complex algorithms, which would be computationally infeasible on pixel level, and creates a spatial support for region-based features.
Superpixel algorithms group pixels into superpixels, “which are local, coherent, and preserve most of the structure necessary for segmentation at scale of interest” [1]. Superpixels should be “roughly homogeneous in size and shape” [1]. Further interesting superpixel approaches mostly targeting still images are described in [2, 3, 4, 5, 9]. Approaches targeting video sequences are described in [3, 6, 7, 8].
Superpixels based on clustering approaches require in general a special post processing step in order to ensure the spatial coherency of the pixels comprised by each superpixel, as the clustering itself does not necessarily lead to spatially coherent superpixels. Such a post processing step can assign each split-off fraction, which is not connected to the main mass of the corresponding superpixel, to its nearest adjacent superpixel (cf. [5,8]). This ensures the spatial connectivity of the pixels comprised in the clusters. Contour evolution approaches like [9, 10] can overcome this drawback in general at the cost of a high number of iterations.